Estimates of approximation rates by Gaussian radial-basis functions

被引：0
|
作者：
Kainen, Paul C. [1 ]
Kurkova, Vera [2 ]
Sanguineti, Marcello [3 ]
机构：
[1] Georgetown Univ, Dept Math, Washington, DC 20057 USA
[2] Inst Comp Sci, Acad Sci Czech Republic, Prague, Czech Republic
[3] Inst Comp Sci, Acad Sci Czech Republic, Prague, Czech Republic
来源：
ADAPTIVE AND NATURAL COMPUTING ALGORITHMS, PT 2 | 2007年 / 4432卷
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Rates of approximation by networks with Gaussian RBFs with varying widths are investigated. For certain smooth functions, upper bounds are derived in terms of a Sobolev-equivalent norm. Coefficients involved are exponentially decreasing in the dimension. The estimates are proven using Bessel potentials as auxiliary approximating functions.
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页码：11 / +
页数：3
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